Time series modeling and forecasting has fundamental importance to various practical domains. Thus a lot of active research works is going on in this subject during several years. The primary objective of time series analysis is to develop a mathematical model that can forecast future observations on the basis of available data. Due to the difficulty in assessing the exact nature of a time series, it is often considerably challenging to generate appropriate forecasts. Over the years, various forecasting models have been developed in literature, of which the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) are widely popular. ARIMA models are well-known for their notable forecasting accuracy and flexibility in representing several different types of time series. Time-Series Prediction and Applications aims to present a comprehensive description of some popular time series forecasting models used in practice, with their salient features. Many important models have been proposed in literature for improving the accuracy and efficiency of time series modeling and forecasting. Twenty-five years ago, exponential smoothing methods were often considered a collection of ad hoc techniques for extrapolating various types of univariate time series. Although exponential smoothing methods were widely used in business and industry, they had received little attention from statisticians and did not have a well-developed statistical foundation. To stay competitive in the global business environment, effective planning regarding scheduling, inventory, production, distribution, purchasing, and so on is very important as it is considered as the backbone of fruitful operations. Appropriate prediction of products plays a pivotal role in reducing unnecessary inventory and smoothing planning issues which result in increasing profit. Many organizations have failed due to the fault estimation. There are enormous research works in the arena of forecasting method selection with time series data. This book serves as valuable guide students, practitioners as well as researchers in business intelligence and stock index prediction.